
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Danger strong hits: 2 | High-risk paths: shells, RCE vectors, exploits | +50 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Block scanning from 119.148.3.101: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
This IP was checked against major DNS-based blacklists used by mail servers and firewalls worldwide.
Checked: Spamhaus, SpamCop, Barracuda, SORBS, CBL, UCEProtect. Results may change over time.
119.148.3.101 has been assigned a threat score of 65/100 (High). This score indicates high threat severity. The IP has shown clear patterns of malicious behavior that warrant immediate defensive measures.
The following attack categories were identified:
The address 119.148.3.101 originates from Dhaka, Bangladesh, operating on the network of Agni Systems Ltd. SUB. It was identified through automated analysis of incoming network traffic across monitored endpoints. The address has been active for 1 days in our monitoring system, producing 29 flagged requests at a rate of ~29/day. This is a residential IP address, suggesting a compromised home device such as a router, smart appliance, or infected workstation participating in a botnet. The IP exhibits directory enumeration behavior, systematically requesting non-existent paths to discover hidden files and misconfigured resources. Bangladesh currently accounts for 102 blocked IPs in our database, making it a significant source of malicious traffic. At 65/100, this IP presents a meaningful threat. Implement rate limiting with escalation to blocking.
This IP is classified as residential, suggesting it may belong to a compromised home device, IoT botnet member, or an infected personal computer. Residential IPs involved in attacks often indicate malware infection without the owner's knowledge.
SQL injection remains one of the most common web attack vectors. Attackers inject malicious SQL code through input fields to extract database contents, modify data, or gain administrative access. Automated scanners test for SQLi vulnerabilities at massive scale.
Internet of Things devices are prime targets for botnet recruitment due to weak default credentials, infrequent updates, and always-on connectivity. Compromised IoT devices generate persistent scanning and attack traffic without their owners knowledge.